Instance-Optimized Data Layouts for Cloud Analytics Workloads
Summary: Introducing MTO, an instance-optimized data-layout framework that jointly blocks across all tables in multi-table cloud workloads (star/snowflake schemas) to maximize block skipping. Leveraging sideways information from joins, it beats single-table layouts, with up to 93% fewer blocks accessed and 75% faster end-to-end queries on a commercial service. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jialin Ding
- 2. Umar Farooq Minhas
- 3. Badrish Chandramouli
- 4. Chi Wang
- 5. Yinan Li
- 6. Ying Li
- 7. Donald Kossmann
- 8. Johannes Gehrke
- 9. Tim Kraska
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|---|---|---|---|---|
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| 6,802 | Understanding Insights into the Basic Structure and Essential Issues of Table Placement Methods in Clusters | 2013 | VLDB | 4.9226626e-05 |
| 2,568 | Towards Cost-Optimal Query Processing in the Cloud | 2021 | VLDB | 8.5239227e-05 |
| 10,385 | Optimizing Block Skipping for High-Dimensional Data with Learned Adaptive Curve | 2025 | SIGMOD | 4.1945683e-05 |
| 8,415 | Pruning in Snowflake: Working Smarter, Not Harder | 2025 | SIGMOD | 4.5197687e-05 |
| 1,611 | Qd-tree: Learning Data Layouts for Big Data Analytics | 2020 | SIGMOD | 0.00011147324 |
| 5,297 | Continuous Cloud-Scale Query Optimization and Processing | 2013 | VLDB | 5.5801669e-05 |
| 3,737 | Skipping-oriented Partitioning for Columnar Layouts | 2017 | VLDB | 6.8033227e-05 |